update to deal with the case when wrong hyper-parameters are given fo…

…r learning curve

machine/learn/skl_utils.py

@@ -1094,7 +1094,8 @@ def plot_imp_score(tmpdir, _id, coefs, feature_names, imp_score_type):
 
 def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
 
-
+    # Plot learning curve
+    print("Plotting learning curve...")
 
 
     features = np.array(features)
@@ -1147,14 +1148,15 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
     # test_scores_std = np.std(test_scores, axis=1)
 
     # if train_sizes.tolist() has nan, then replace it with 0
-    # check if train_sizes.tolist has nan
-    if np.isnan(train_sizes.tolist()).any():
+    # check if all of train_sizes.tolist has nan
+
+    if np.isnan(train_sizes.tolist()).all():
         #replace nan with -1
         train_sizes = np.nan_to_num(train_sizes, nan=-1)
-    if np.isnan(train_scores.tolist()).any():
+    if np.isnan(train_scores.tolist()).all():
         # replace nan with -1
         train_scores = np.nan_to_num(train_scores, nan=-1)
-    if np.isnan(test_scores.tolist()).any():
+    if np.isnan(test_scores.tolist()).all():
         # replace nan with -1
         test_scores = np.nan_to_num(test_scores, nan=-1)
 
@@ -1174,6 +1176,8 @@ def plot_learning_curve(tmpdir,_id,model,features,target,cv,return_times=True):
 
 
 def plot_pca_2d(tmpdir,_id,features,target):
+
+    print("plot_pca_2d")
     # import numpy as np
     # import matplotlib.pyplot as plt
 
@@ -1475,6 +1479,8 @@ def plot_tsne(tmpdir,_id,features,target):
     # print(X)
     # print(y)
 
+    print("Plotting t-SNE")
+
     tsne = TSNE(n_components=2, verbose=1, random_state=123)
     X_2d = tsne.fit_transform(X)
 
@@ -1510,7 +1516,12 @@ def plot_tsne(tmpdir,_id,features,target):
     # path = tmpdir + _id + '/tsneJson_' + _id + '.json'
     # import json
 
+    # X_2d
+    print("X_2d",X_2d)
+    print("y",y)
 
+    X_2d = [1]
+    y = [1]
 
     # save X and y to json file
     tsne_dict = {